Adaptive image improvement

ABSTRACT

A method includes analyzing an input image at least to determine locations of human skin in the input image and processing the input image at least to improve, on a per pixel basis, the areas of human skin of the input image. Another method included in the present invention includes measuring blurriness levels in an input image; and processing the input image with the blurriness levels at least to sharpen the input image. A third method includes identifying areas of at least bright light in an input image and changing the sharpness of the input image as a function of exposure level of different areas of the input image.

FIELD OF THE INVENTION

The present invention relates to still images generally and to theirimprovement in particular.

BACKGROUND OF THE INVENTION

Digital images are well known and are generated in many ways, such asfrom a digital camera or video camera (whether operated automatically orby a human photographer), or scanning of a photograph into digitalformat. The digital images vary in their quality, depending on theabilities of the photographer as well as on the selected exposure, theselected focal length and the lighting conditions at the time the imageis taken.

Digital images may be edited in various ways to improve them. Forexample, the image may be sent through a processor which may enhance thesharpness of the image by increasing the strength of the high frequencycomponents. However, the resultant image may have an increased level ofnoise, spurious oscillations known as “ringing” which are caused byovershooting or undershooting of signals and image independent sharpnessenhancement that results in an incorrect change in sharpness.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1 is a block diagram illustration of an adaptive image improvementsystem, constructed and operative in accordance with the presentinvention;

FIG. 2 is a block diagram illustration of an image analyzer forming partof the system of FIG. 1;

FIG. 3 is a block diagram illustration of a controller forming part ofthe system of FIG. 1;

FIG. 4 is a block diagram illustration of a human skin processing unitforming part of the system of FIG. 1;

FIG. 5 is a block diagram illustration of a combined noise reducer andvisual resolution enhancer, forming part of the system of FIG. 1;

FIG. 6 is a graphical illustration of the response of low and high passfilters, useful in the system of FIG. 1;

FIG. 7 is a graphical illustration of the response of a limiter usefulin the combined noise reducer and visual resolution enhancer of FIG. 5.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

Reference is now made to FIG. 1, which illustrates an adaptive imageimprovement system, constructed and operative in accordance with thepresent invention. The system of the present invention may compensatefor the differences between how an image sensor, such as a video camera,views an object and how the human visual system views the same object,producing an image that generally is pleasing to people. The presentinvention may be operative to improve on the output of digital stillcameras, printers, internet video, etc.

In particular, the system of FIG. 1, which may comprise an imageanalyzer 10, a controller 12, a human skin processing unit 14, a noisereducer 16 and a visual resolution enhancer 18, may operate, at least inpart, to improve images, indicated by (YC_(r)C_(b)), as well as tominimize the undesired effects of common processing operations.

For example, Applicants have realized that the details of human skingenerally should be sharpened less than other details. Moreover, for lowlight exposures, image sensors typically generate human skin areas whichare significantly redder than as seen by the human visual system. Tohandle both of these issues, image analyzer 10 may detect areas of humanskin in the input image. Human skin processing unit 14 may reduce thesaturation of the detected areas of human skin in the image, thereby toreduce the redness of the skin, and visual resolution enhancer 18 maychange the high frequency components of areas of the detected human skinto attempt to reduce the sharpness of those areas in the final image.

Applicants have further realized that the ‘ringing’ effect may occurbecause the processing may change the intensities of objects or detailsin the input image so much that they ‘overshoot’ or ‘undershoot’ theintensities that originally were in the object. Applicants have realizedthat the overshooting and undershooting may be reduced by diminishingthe intensity levels of those high frequency components whose intensitylevels are above, respectively, a threshold.

Furthermore, Applicants have realized that the amount of texture on thedetails of the image is an important parameter for the sharpness of lowcontrast, small details. Therefore, in accordance with a preferredembodiment of the present invention, image analyzer 10 may determine thetexture level in the details of the image and visual resolution enhancer18 may operate to increase them if necessary.

Image analyzer 10 may detect areas of human skin in the input image, andmay estimate the amount of low contrast, small details (texture) in theimage. Image analyzer 10 may generate an indication of duration of edgesat each pixel. In addition, analyzer 10 may determine the locations ofdetails of high brightness and of low brightness, since noise isgenerally more noticeable in blacker areas, which have low light.Controller 12 may use the analysis to determine a set of parameters tocontrol units 14, 16 and 18. Some of these parameters are global, othersare per pixel parameters.

Using the parameters produced by controller 12, skin processing unit 14may process the areas of the input image which have skin in them. Forlow light exposures, areas of human skin may be oversaturated (i.e. thechrominance of such areas may be too high relative to the luminancecomponents). Accordingly, skin processing unit 14 may reduce thechrominance values of such areas. It will be appreciated that an imagewith no human features in it would pass through unit 14 unedited.

Once the skin details have been processed, noise reducer 16 may reducethe noise in the high frequency components to provide sharpnessenhancement without an increase in the visibility of the noise. Finally,visual resolution enhancer 18 may sharpen the output of noise reducer 16and may operate to increase the spatial depth of the image, as well asits field of view, producing the processed image, indicated by(Y_(p)C_(rp)C_(bp)).

Reference is now made to FIG. 2, which illustrates an exemplaryembodiment of image analyzer 10, constructed and operative in accordancewith the present invention. In this embodiment, analyzer 10 may comprisea skin analyzer 30, a texture analyzer 32, a sharpness analyzer 34 and abrightness analyzer 36.

Skin analyzer 30 may determine the presence of human skin in the imageand may generate a mask SK(i,j) marking the locations of the skin. Skinanalyzer 30 may comprise a skin detector 40, a 2D low pass filter 42 anda skin mask generator 44.

Applicants have discovered empirically that most skin, except those withvery high pigment levels, have chrominance levels within specificdynamic ranges. Thus, skin detector 40 may analyze the chrominancesignals C_(r)(i,j) and C_(b)(i,j) as follows to determine the locationh_(s)(i,j) of not very dark human skin:

${h_{s}\left( {i,j} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{20mu}\frac{C_{b}\left( {i,j} \right)}{C_{s}\left( {i,j} \right)}} \in {D_{s}\mspace{14mu}{and}\mspace{11mu}{C_{r}\left( {i,j} \right)}} \in {D_{rs}\mspace{11mu}{and}\mspace{11mu}{C_{b}\left( {i,j} \right)}} \in D_{bs}} \\0 & {otherwise}\end{matrix} \right.$where D_(s), D_(rs) and D_(bs) are the dynamic ranges for most humanskin for

$\frac{C_{b}}{C_{r}},$C_(r) and C_(b), respectively. Applicants have determined empiricallythat, for many images:D_(s)={0.49, . . . 0.91}D_(rs)={89, . . . , 131}D_(bs)={144, . . . 181}

2D low pass filter 42 may be any suitable low pass filter and may filterthe signal h_(s) to remove noise and any random pixels, such as may comefrom non-skin areas that happen to meet the criteria but are not skin.An exemplary response for low pass filter 42 may be seen in FIG. 6, towhich reference is now briefly made. FIG. 6 also shows an exemplaryresponse for high pass filters which may be used in the presentinvention.

Finally, skin mask generator 44 may generate skin mask SK(i,j) to have a1 in those locations where the filtered skin signal h_(s)′ is above apredetermined threshold SKIN (e.g. 3-5 quant (8 bit/pel)).

Since texture components are high frequency components of the luminancesignal Y, texture analyzer 32 may comprise a high pass filter 50. Anexemplary high pass filter may be that shown in FIG. 6. Analyzer 32 mayalso comprise a comparator 52 and a texture estimator 54. Comparator 52may compare the high frequency signal V_(HF) to a base threshold levelTHD₀. In one embodiment, base texture threshold level THD₀ is 3 σ, whereσ is a noise dispersion level. For example, σ may be 1-2 quant (8bit/pel).

For each pixel (i,j) whose V_(HF) is below base texture threshold levelTHD₀, a variable n_(i,j) may receive the value 1. The remaining pixelsmay receive a 0 value.

Texture estimator 54 may generate a global texture level θ defined asthe percentage of pixels in the image below the texture threshold THD₀:

$\theta = \frac{\sum\limits_{i}^{\;}{\sum\limits_{j}^{\;}n_{i,j}}}{N*M}$where N and M are the number of pixels in the horizontal and verticaldirections, respectively.

Sharpness analyzer 34 may comprise four concatenated delays 60, fourassociated adders 62 and a sharpness estimator 64. A sharp image hasedges of detail that change sharply from one pixel to the next. However,the edges in a blurry image occur over many pixels. Delays 60 and adders62 may generate signals indicating how quickly changes occur.

Each delay 60 may shift the incoming luminance signal Y by one pixel(thus, the output of the fourth adder may be shifted by four pixels) andeach adder 62 may subtract the delayed signal produced by its associateddelay 60 from the incoming luminance signal Y. The resultant signals D1,D2, D3 and D4 may indicate how similar the signal is to its neighbors.

Sharpness estimator 64 may take the four similarity signals D1, D2, D3and D4 and may determine a maximum value Dmax of all the signals D1, D2,D3 and D4, and may determine four per pixel signals SH1(i,j), SH2(i,j),SH3(i,j) and SH4(i,j) indicating that the edge duration at that pixel is1, 2, 3 or 4 pixels, respectively, as follows:SH1(i,j)=1 if D1(i,j)=DmaxSH2(i,j)=1 if D2(i,j)=DmaxSH3(i,j)=1 if D3(i,j)=DmaxSH4(i,j)=1 if D4(i,j)=Dmax

Finally, brightness analyzer 36 may determine the locations of low andbright light and may comprise a low pass filter 70, a low light maskgenerator 72, a bright light mask generator 74 and a bright lightcoefficient definer 76. Low pass filter 70 may be any suitable low passfilter, such as that shown in FIG. 6, and may generate a low frequencysignal V_(LF). Low light mask generator 72 may review low frequencysignal V_(LF) to determine the pixels therein which have an intensitybelow a low light threshold LL. For example, LL might be 0.3 Y_(max),where Y_(max) is the maximum allowable intensity value, such as 255.Generator 72 may then generate a mask MASK_(LL) with a positive value,such as 255, for each of the resultant pixels.

Bright light mask generator 74 may operate similarly to low light maskgenerator 72 except that the comparison is to a bright light thresholdHL above which the intensities should be and the mask may be MASK_(HL).For example, threshold HL might be 0.7 Y_(max). Bright light coefficientgenerator 76 may generate a per pixel coefficient K_(HL)(i,j) asfollows:

${K_{HL}\left( {i,j} \right)} = {\left\lbrack {1 + \frac{Y\left( {i,j} \right)}{Y_{\max}}} \right\rbrack{{MASK}_{HL}\left( {i,j} \right)}}$Per pixel coefficient K_(HL)(i,j) may be utilized to increase sharpnessfor bright light pixels.

Reference is now made to FIG. 3, which illustrates the operation ofcontroller 12. Controller 12 may convert the parameters of analyzer 10into control parameters for human skin processing unit 14, noise reducer16 and visual resolution enhancer 18.

Controller 12 may generate a low light skin mask FSK(i,j) which combinesboth skin mask SK and low light mask MASK_(LL). In the presentinvention, only those pixels which both relate to skin and are in lowlight may be processed differently. Thus, low light skin mask FSK(i,j)may be generated as:FSK(i,j)=SK(i,j)*MASK_(LL)(i,j)

Controller 12 may generate a visual perception threshold THD above whichthe human visual system may be able to distinguish details. In thisembodiment, the details are texture details or contrast small details.Since this threshold is a function of the amount θ of texture in theimage, the threshold may be generated from base threshold THDo asfollows:THD=THD₀(1+θ)

Controller 12 may determine a per pixel, visual resolution enhancement,texture coefficient K_(t)(i,j). This coefficient affects the highfrequency components of the image which may be affected by the amount oftexture θ as well as the brightness level K_(HL) and may operate toincrease the spatial depth and field of view of the image.

${K_{t}\left( {i,j} \right)} = \begin{matrix}{{K_{t0}\left( {1 - \frac{\theta}{2}} \right)}{K_{HL}\left( {i,j} \right)}} & {{{if}\mspace{14mu}{{MASK}_{HL}\left( {i,j} \right)}} = 1} \\{{K_{t0}\left( {1 - \frac{\theta}{2}} \right)}} & {{{if}\mspace{14mu}{{MASK}_{HL}\left( {i,j} \right)}} = 0}\end{matrix}$where K_(t0) may be a minimum coefficient level defined from apre-defined, low noise image. For example, K_(t0) may be 2-3.

Another per pixel, visual resolution enhancement coefficient,K_(sh)(i,j), may operate to improve sharpness. Through sharpnesscoefficient K_(sh), the high frequency components of blurry edge pixelsmay be increased, thereby sharpening them. The sharpening level ishigher for blurry edges and lower for already sharp edges. Controller 12may generate a preliminary matrix K_(s)(i,j) from the sharpnessestimates SH1, SH2, SH3 and SH4, as follows:

${K_{s}\left( {i,j} \right)} = \left\{ \begin{matrix}{C_{4}K_{sh0}} & {{{if}\mspace{14mu}{SH}\; 4\left( {i,j} \right)} = 1} \\{C_{3}K_{sh0}} & {{{if}\mspace{14mu}{SH}\; 3\left( {i,j} \right)} = 1} \\{C_{2}K_{sh0}} & {{{if}\mspace{14mu}{SH}\; 2\left( {i,j} \right)} = 1} \\{C_{1}K_{sh0}} & {{{if}\mspace{14mu}{SH}\; 1\left( {i,j} \right)} = 1} \\C_{0} & {otherwise}\end{matrix} \right.$where K_(sh0) may be a maximum coefficient level defined from apre-defined, low noise image. For example, K_(sh0) may be 2 . . . 4. TheC_(i) may be higher for blurry edges (e.g. SH4=1) and lower for sharperedges (e.g. SH1=1). For example:C_(i)={0,0.25,0.5,0.75,1},i=0 . . . 4

Controller 12 may produce the final coefficient K_(sh)(i,j) by includingthe effects of brightness (in matrix K_(HL)(i,j)) to preliminarycoefficient K_(s)(i,j):

${K_{sh}\left( {i,j} \right)} = \begin{matrix}{{K_{s}\left( {i,j} \right)}*{K_{HL}\left( {i,j} \right)}} & {{{if}\mspace{14mu}{MASK}_{HL}} = 1} \\{{K_{s}\left( {i,j} \right)}\mspace{121mu}} & {{{if}\mspace{14mu}{MASK}_{HL}} = 0}\end{matrix}$

Controller 12 may generate a skin blurring mask K_(sk) for visualresolution enhancer 18. Wherever skin mask SK(i,j) indicates that thecurrent pixel has skin in it, skin blurring mask K_(sk)(i,j) may have areduction coefficient, as follows:

${K_{sk}\left( {i,j} \right)} = \begin{matrix}{K_{sk0}{{SK}\left( {i,j} \right)}} & {{{if}\mspace{20mu}{{SK}\left( {i,j} \right)}} = 1} \\1 & {{{if}\mspace{14mu}{{SK}\left( {i,j} \right)}} = 0}\end{matrix}$where K_(sk0) may be a desired sharpness reduction coefficient for humanskin, such as 0.5.

With the control parameters FSK, THD, K_(sh), K_(t) and K_(sk),controller 12 may control the operation of skin processing unit 14,noise reducer 16 and visual resolution enhancer 18. FIGS. 4 and 5illustrate the operations of units 14, 16 and 18.

Reference is now made to FIG. 4, which illustrates the operation of skinprocessing unit 14. Unit 14 may operate to lower the saturation levelsof areas of human skin. Since chrominance levels C_(r) and C_(b)represent the saturation in the input image, unit 14 may operate onthem. However, in many systems, such as digital video broadcast systems,chrominance levels C_(r) and C_(b) have an offset value, such as of 128,which must be removed before processing. To that end, unit 14 maycomprise an offset remover 106 to remove the offset, creating signalsC_(r0) and C_(b0), and an offset restorer 108 to restore it. Theimproved chrominance signals may be noted as C_(rp) and C_(bp).

In addition, unit 14 may comprise a coefficient generator 100, a switch102 and two multipliers 104A and 104B. Coefficient generator 100 maygenerate a color saturation coefficient K_(cs), to change the saturationof skin pixels, as follows:

${{K_{cs}\left( {i,j} \right)} = {{K_{cs0}\left( {1 - \frac{Y\left( {i,j} \right)}{0.3\mspace{11mu} Y_{\max}}} \right)} + \frac{Y\left( {i,j} \right)}{0.3\mspace{11mu} Y_{\max}}}},{0 \leq {Y\left( {i,j} \right)} \leq {0.3\mspace{11mu} Y_{\max}}}$where K_(cs0) is a minimum human skin saturation level, such as 0.7.

Switch 102 may select the amplification for multipliers 104 for thecurrent pixel (i,j). When low light skin mask FSK(i,j) indicates thatthe current pixel has both a low light level and skin in it (i.e.FSK(i,j)=1), then switch 102 may provide the color saturationcoefficient K_(cs)(i,j) for the current pixel. Otherwise, switch 102 mayprovide a unity value (e.g. 1) to multipliers 104. Thus, when thecurrent pixel (i,j) has skin in it, skin processing unit 14 may changeits saturation level by changing the intensity levels of chrominancesignals C_(r0) and C_(b0).

Reference is now made to FIG. 5, which illustrates a combined noisereducer and visual resolution enhancer, labeled 110, which operates onthe luminance signal Y. Unit 110 does not affect chrominance signalsC_(rp) and C_(bp) produced by skin processing unit 14 since, as iswell-known, image sharpness may be defined by the luminance signal Y.

Unit 110 may divide luminance signal Y into three channels, a lowfrequency channel (using a 2D low pass filter 112, such as that of FIG.6) and two high frequency channels, one for the vertical direction(using a high pass filter 114V, such as that of FIG. 6) and one for thehorizontal direction (using a high pass filter 114H, such as that ofFIG. 6).

For each high frequency channel, there is a limiter 116, two multipliers118 and 119, a low pass filter 120, two adders 122 and 123 and anon-linear operator 124.

Each limiter 116 may have any suitable amplitude response. An exemplaryamplitude response may be that shown in FIG. 7, to which reference isnow briefly made, in which the output is linear until the thresholdlevel THD (where threshold THD is an input from controller 12) at whichpoint the output is null (e.g. 0).

Since threshold level THD is a texture threshold, each limiter 116 mayselect those texture details, which are low contrast, small detailsfound in the high frequency signal V_(HF), which the human eye may onlydetect. Adders 122 may subtract the limited signal from the highfrequency signal V_(HF) to generate signals with contrasting smalldetails that may also be distinguished by the human eye.

Non-linear operators 124 may operate on the signals with thedistinguishable small details, output from adders 122, to reduce theirintensity levels so as to reduce the possibility of over/undershootingafter sharpness enhancement. Non-linear operators 124 may more stronglyreduce high levels of the signal than lower levels of the signals. Forexample, the multiplication coefficients may be defined as follows:

${K_{NL}\left( {i,j} \right)} = {1 - {\left( {1 - K_{NL0}} \right)\frac{V_{in}\left( {i,j} \right)}{V_{{in},\max}}}}$where V_(in)(i,j) may be the input signal to operators 124, V_(in,max)may be the maximum possible value of V_(in), such as 255, and, K_(NL0)may be a user defined value to provide protection against ringing. Inone embodiment, K_(NL0) might be 0.

Multipliers 119 may change values per pixel, as per the informationprovided by parameter K_(sh)(i,j), and may provide sharpness enhancementto the output of non-linear operators 124.

The texture signals generated by limiters 116 may be further processedby multiplier 118, using per pixel, enhancement coefficient K_(t)(i,j).Since such amplification may increase the noise level, the output ofmultipliers 118 may then be processed through low pass filters 120 toreduce the noise level. It is noted that low pass filter 120H of thehorizontal channel is a vertical low pass filter and low pass filter120V of the vertical channel is a horizontal low pass filter.

Unit 110 may then add the processed texture signals with the sharpeneddistinguished signals in adders 123 to produce the high frequencyhorizontal and vertical components. Unit 110 may then add these highfrequency components together in an adder 126. The resultant highfrequency signal may be processed, in a multiplier 128, to reduce thesharpened high frequency signals for those pixels with skin in them. Thereduction coefficient for multiplier 128 may be skin blurring maskK_(SK)(i,j).

An adder 130 may add the processed high frequency components to the lowfrequency components (output of low pass filter 112) together to providean improved luminance signal Y_(p).

It will be appreciated that the improved signals (Y_(p), C_(rp), C_(bp))may provide a sharpened image which is more pleasant to the human eyethan those of the prior art. The output of the present invention may besharpened but it may have little or no ringing, little or no overlysharpened skin details and reduced noise.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

1. A system comprising: an image analyzer to measure a blurriness levelin an input image, said image analyzer comprising a sharpness detectorconfigured to indicate blurriness of one or more edges of the inputimage over a range of one or more pixels; and a processing unitconfigured to sharpen said input image using said blurriness level, saidprocessing unit comprising a coefficient generator configured to utilizeoutput of said sharpness detector to generate one or more pixelsharpening coefficients.
 2. The system of claim 1, wherein said one ormore pixel sharpening coefficients are a function of an edge's durationover one or more pixels.
 3. The system of claim 2, wherein said one ormore pixel sharpening coefficients comprise at least two different pixelsharpening coefficients, each of said at least two different pixelsharpening coefficients being associated with a different input imagelocation brightness.
 4. The system of claim 1, said processing unitfurther comprising a multiplier configured to multiply high frequencycomponents of said input image by at least one of said one or more pixelsharpening coefficients.
 5. A method comprising: measuring a blurrinesslevel in an input image, said measuring comprising indicating blurrinessof one or more edges of the input image over a range of one or morepixels; and processing said input image, using said blurriness level, atleast to sharpen said input image, said processing comprisinggenerating, based at least in part on said blurriness level, one or morepixel sharpening coefficients.
 6. The method according to claim 5,wherein said one or more pixel sharpening coefficients are a function ofan edge's duration over one or more pixels.
 7. The method according toclaim 6, wherein said one or more pixel sharpening coefficients compriseat least two different pixel sharpening coefficients, each of said atleast two different pixel sharpening coefficients being associated witha different input image location brightness.
 8. The method of claim 5,said processing further comprising multiplying high frequency componentsof said input image by at least one of said one or more pixel sharpeningcoefficients.
 9. A system comprising: an image analyzer configured to doone or both of: measure a blurriness level in an input image and, basedat least in part on a measured blurriness level, determine at least afirst parameter; or identify areas of bright light in said input imageand, based at least in part on said areas of bright light, determine atleast a second parameter; and a processing unit configured to sharpensaid input image based on one or both of said first or secondparameters, said processing unit comprising a coefficient generatorconfigured to generate one or more pixel sharpening coefficientsutilizing one or both of said first or second parameters.
 10. The systemof claim 9, wherein said image analyzer comprises a sharpness detectorconfigured to indicate blurriness of one or more edges of the inputimage over a range of one or more pixels.
 11. The system of claim 9,wherein said processing unit further comprises a multiplier configuredto multiply high frequency components of said input image by at leastone of said one or more pixel sharpening coefficients.
 12. The system ofclaim 9, wherein said second parameter is associated with a bright lightmask for pixels of said input image at or above a predefined brightlight level.
 13. The system of claim 12, wherein said coefficientgenerator is further configured to generate one or more pixel sharpeningcoefficients for said pixels of said input image at or above saidpredefined bright light level.
 14. A method comprising: analyzing aninput image, wherein analyzing comprises one or both of measuring ablurriness level in said input image or identifying an area of brightlight in said input image; determining one or more parameters based onsaid analyzing; and changing a sharpness of said input image based atleast in part on said one or more parameters, wherein said changingcomprises generating one or more pixel sharpening coefficients utilizingat least one of said one or more parameters.
 15. The method of claim 14,wherein said changing further comprises multiplying high frequencycomponents of said input image by at least one of said one or more pixelsharpening coefficients.
 16. The method of claim 14, wherein at leastone of said one or more parameters is associated with a bright lightmask for pixels of said input image at or above a predefined brightlight level.
 17. The method of claim 16, wherein said changing furthercomprises generating one or more pixel sharpening coefficients for saidpixels of said input image at or above said predefined bright lightlevel.